Machine Learning

The human foot forward into the 21st century has ushered the big guns of globalisation and modernisation to severely impact every step taken. Numerous products, commodities, amenities and services are at our disposal making the desire to availability mapping incredibly robust.

A Recommendation Engine plays a vital role in increasing the chances of a user buying a product. In today’s world, with tons of data from searched products available (thanks to the digital data explosion), it is very easy to find what people are likely to buy – just by looking at their ‘intent’ data.

Not far from now, you will have hard time in figuring out whether this blog is written by an machine or a human! Machine learning, the recent buzz word in the tech industry have started closing the intelligence gap between humans and machine.

Developers are often interested in learning about the software industry’s best practices, so that they can improve the robustness and efficiency of their code. The best way to learn is by reading the source code of programs that are in production and running heavy workloads.

Consider a simple problem Insurance sector problem of recommending a product to a potential customer. The recommendation is based on certain customer attributes, similar to predictive analytics in target marketing.

For classifying or clustering data in the context of a machine learning problem, the first step is to create a representation of data, usually called the Feature Vector. Datasets consisting of images or audio files have feature vectors that are already in numeric form.

Analytics is the discovery of meaningful insights and communicating it in an efficient way using the data available. The advancement in analytics has led to the development of various techniques which help make people use their resources optimally.